Advanced Multimedia Processing Lab -- Group Member -- Cha Zhang

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Group Member

Cha Zhang
PhD. Student

Personal Homepage: http://www.andrew.cmu.edu/~czhang
Email: czhang@andrew.cmu.edu

Office: Porter Hall B10
Lab: Porter Hall B42
Phone: 412-268-7114
Fax: 412-268-1679
Mailing Address:
Department of ECE, Carnegie Mellon University,
5000 Forbes Avenue, Pittsburgh,
PA 15213-3890

[Research Interests]        [Projects]      [Publications

Research Interests

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Projects
  The Mobile Camera Array

We build a mobile camera array consisting of 48 network cameras. Virtual views can be rendered from the 48 cameras in real time (5-10 fps). Moreover, a unique feature of our camera array is that the cameras are mobile. Each camera can move horizontally, as well as pan. This mobile camera array opens a wide area of novel research topics.

See the mobile camera array project page for more information!

Active Image-Based Rendering

We build a system for capturing Image-based rendering scenes intelligently. The system first use a simple algorithm to estimate the rendering quality of each neighborhood. Then it decides the position where more images need to be taken. It takes more images, and then go back to to the first step to find the next position. This iteration runs until the maximum number if images reaches. The result is a non-uniformly sampled IBR. 

Please refer to the active image-based rendering project page for more details. 

Spectral Analysis and Sampling for Image-Based Rendering

Image-based rendering has become a very active research area in recent years. The sampling problem for IBR has not been completely solved. We study the the spectral analysis and sampling of IBR. In terms of spectral analysis, we obtain more general results than previous work by using the surface plenoptic function. After we get the spectrum, generalized sampling for high-dimensional data is used to pack the data in the frequency domain.  

For more detailed description, please see the IBR sampling project page

Active Learning in Content-Based Information Retrieval

We propose to use active learning to improve the efficiency of hidden annotation. Active learning has been studied in the machine learning literature. For many types of machine learning algorithms, one can find the statistically "optimal" way to select the training data. This was given the name active learning. Although in traditional machine learning research, the learner typically works as the recipient of data to do training, active learning enables the learner to use his own ability to respond, to collect data, and to influence the world he is trying to understand. To be more precise, what we are interested here is a specific form of active learning, i.e., selective sampling. The goal of selective sampling is to reduce the number of training examples that need to be labeled by examining unlabeled examples and selecting the most informative ones for the human to annotate. 

For more details on the project, please see the active learning project page

3D Model Retrieval

In our system, we propose a new set of features that view the 3D model as a solid binary region. Ten features such as volume-surface ratio, aspect ratio, moment invariants and Fourier transform coefficients are extracted. In the current version, we simply use Euclidean distance to measure the similarity between two models after the features are normalized. 

For more details on the project, please see the 3D model retrieval project page

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Publications

Journal Papers and Book Chapters: 

Technical Reports: 

Conference Papers: 

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Revised: August 09, 2009 .